The Sequence Knowledge #736: Can Chain of Thought Monitoring Help AI Interpretability
Exploring one of the newest theories of AI interpretability.
Today we will Discuss:
How can chain of thought monitoring(CoT) influence AI interpretability.
Anthropic’s famous paper about how LLMs don’t always say what they think.
💡 AI Concept of the Day: Chain of Thought and Interpretability
Chain-of-thought (CoT) monitoring sits at the intersection of interpretability and oversight: it promises a window into a model’s intermediate reasoning while giving us a handle for detecting misbehavior. The catch is faithfulness—whether the text a model writes as its “thoughts” actually reflects the causal path to its answer. Early evidence showed CoTs can be plausible yet unfaithful rationalizations, cautioning against naïvely trusting them. More recently, large-scale tests on modern reasoning models report that CoTs often omit the very cues that drove a solution, especially under optimization pressure. Together, these results frame CoT as a powerful but fragile signal—not a ground truth.